Synthèse
Agentic AI is more than LLM inference. Its enterprise value depends on the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure. An agent is a goal-driven automated enterprise workflow process: It plans a multi-step task, calls tools, reads results, and retries when something fails. Enterprise agents are therefore not just an inference problem; they are a systems problem. Most agentic AI metrics focus on evaluating the LLM used
Agentic AI is more than LLM inference. Its enterprise value depends on the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure. An agent is a goal-driven automated enterprise workflow process: It plans a multi-step task, calls tools, reads results, and retries when something fails. Enterprise agents are therefore not just an inference problem; they are a systems problem. Most agentic AI metrics focus on evaluating the LLM used.
Platform teams also need to know how long the tasks take, how many agents a fleet can support, what users experience at the end of the execution process, and how costs change as more agents work simultaneously. A more useful enterprise view looks at six metrics: Task success rate Cost per task Time per task Task throughput Agent density (agents per vCPU) Latency Together, these answer the questions enterprise AI operators care about: Is the system performing as expected? How many agents can the system sustain? How should it scale to support more agents?